An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning

Unsupervised sentence embeddings learning has been recently dominated by contrastive learning methods (e.g., SimCSE), which keep positive pairs similar and push negative pairs apart. The contrast operation aims to keep as much information as possible by maximizing the mutual information between positive instances, which leads to redundant information in sentence embedding. To address this problem, we present an information minimization based contrastive learning (InforMin-CL) model to retain the useful information and discard the redundant information by maximizing the mutual information and minimizing the information entropy between positive instances meanwhile for unsupervised sentence representation learning. Specifically, we find that information minimization can be achieved by simple contrast and reconstruction objectives. The reconstruction operation reconstitutes the positive instance via the other positive instance to minimize the information entropy between positive instances. We evaluate our model on fourteen downstream tasks, including both supervised and unsupervised (semantic textual similarity) tasks. Extensive experimental results show that our InforMin-CL obtains a state-of-the-art performance.Comment: 11 pages, 3 figures, published to COLING 202

Triticeae crop genome biology: an endless frontier

Triticeae, the wheatgrass tribe, includes several major cereal crops and their wild relatives. Major crops within the Triticeae are wheat, barley, rye, and oat, which are important for human consumption, animal feed, and rangeland protection. Species within this tribe are known for their large genomes and complex genetic histories. Powered by recent advances in sequencing technology, researchers worldwide have made progress in elucidating the genomes of Triticeae crops. In addition to assemblies of high-quality reference genomes, pan-genome studies have just started to capture the genomic diversities of these species, shedding light on our understanding of the genetic basis of domestication and environmental adaptation of Triticeae crops. In this review, we focus on recent signs of progress in genome sequencing, pan-genome analyses, and resequencing analysis of Triticeae crops. We also propose future research avenues in Triticeae crop genomes, including identifying genome structure variations, the association of genomic regions with desired traits, mining functions of the non-coding area, introgression of high-quality genes from wild Triticeae resources, genome editing, and integration of genomic resources

Research on singular spectrum decomposition and its application to rotor failure detection

As an important part of rotating machinery, a healthy rotor is critical to ensuring optimal working conditions of the entire system. Considering that the vibration signal of rotor consists of different frequency components when the failure arises, a novel rotor failure detection method based on singular spectrum decomposition (SSD) is presented. The original vibration signal is adaptively decomposed into a number of singular spectrum components (SSCs) by the SSD method. Then, energy separation algorithm (ESA) is adopted to demodulate each singular spectrum component. Finally, the SSD-ESA time-frequency spectrum can be obtained and the fault features contained in the SSD-ESA time-frequency spectrum can be identified to determine the fault types. The effectiveness of SSD for harmonic separation was assessed through tones separation analyses, the results show that SSD is able to separate more harmonic pairs of different amplitude ratios than empirical mode decomposition (EMD). Furthermore, three simulations of multi-component signals were designed to investigate the use of SSD for signal decomposition. The SSD method was then applied to detect signatures caused by rotor oil film whirl in experimental signals and compared to both EMD and ensemble EMD (EEMD). The simulated analysis results reflect that SSD shows superiority to EMD and EEMD in inhibiting mode mixing and extracting the time-varying frequency components. The experimental analysis results demonstrate that the SSD based rotor failure detection method is an alternative method under both constant and variable speed conditions

Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis

A time-frequency analysis method based on improved variational mode decomposition and Teager energy operator (IVMD-TEO) is proposed for fault diagnosis of turbine rotor. Variational mode decomposition (VMD) can decompose a multicomponent signal into a number of band-limited monocomponent signals and can effectively avoid model mixing. To determine the number of monocomponents adaptively, VMD is improved using the correlation coefficient criterion. Firstly, IVMD algorithm is used to decompose a multicomponent signal into a number of monocompositions adaptively. Second, all the monocomponent signals’ instantaneous amplitude and instantaneous frequency are demodulated via TEO, respectively, because TEO has fast and high precision demodulation advantages to monocomponent signal. Finally, the time-frequency representation of original signal is exhibited by superposing the time-frequency representations of all the monocomponents. The analysis results of simulation signal and experimental turbine rotor in rising speed condition demonstrate that the proposed method has perfect multicomponent signal decomposition capacity and good time-frequency expression. It is a promising time-frequency analysis method for rotor fault diagnosis

Performance Entitlement by Using Novel High Strength Electrical Steels and Copper Alloys for High-Speed Laminated Rotor Induction Machines

The laminated rotor Induction Machine (IM), with its simple construction and manufacturing, robustness, ease of control and comparatively lower cost remains by far the most utilized electromechanical energy converter. At very high speeds, traditionally its use is considered to be limited to the previously established operational limits of 2.5 × 105 rpm√kW, beyond which the surface Permanent Magnet (PM) Machine and the solid rotor Induction Machine become the machines available for consideration. The aforesaid limits are derived from the use of classic materials. This paper reviews the recent developments in electrical steels and copper alloys and translates these into the resulting performance entitlement and operational limits through a case study involving a marine application, for which an existing rare-earth PM machine is in use. It is concluded that with novel materials, laminated rotor induction machines can be operated up to 6 × 105 rpm√kW, thus opening the use of the rare-earth free Induction Machine for a wider application range previously limited to PM machines

Global systematic review with meta-analysis shows that warming effects on terrestrial plant biomass allocation are influenced by precipitation and mycorrhizal association

Biomass allocation in plants is fundamental for understanding and predicting terrestrial carbon storage. Yet, our knowledge regarding warming effects on root: shoot ratio (R/S) remains limited. Here, we present a meta-analysis encompassing more than 300 studies and including angiosperms and gymnosperms as well as different biomes (cropland, desert, forest, grassland, tundra, and wetland). The meta-analysis shows that average warming of 2.50 °C (median = 2 °C) significantly increases biomass allocation to roots with a mean increase of 8.1% in R/S. Two factors associate significantly with this response to warming: mean annual precipitation and the type of mycorrhizal fungi associated with plants. Warming-induced allocation to roots is greater in drier habitats when compared to shoots (+15.1% in R/S), while lower in wetter habitats (+4.9% in R/S). This R/S pattern is more frequent in plants associated with arbuscular mycorrhizal fungi, compared to ectomycorrhizal fungi. These results show that precipitation variability and mycorrhizal association can affect terrestrial carbon dynamics by influencing biomass allocation strategies in a warmer world, suggesting that climate change could influence belowground C sequestration

Genotypes and Phenotypes of DMD Small Mutations in Chinese Patients With Dystrophinopathies

Dystrophinopathies are a group of neuromuscular disorders resulting from mutations in DMD, including Duchenne muscular dystrophy (DMD), intermediate muscular dystrophy (IMD), and Becker muscular dystrophy (BMD). Herein, we present the characteristics of small mutations in Chinese patients with dystrophinopathies, and explore genotype–phenotype correlations. In our cohort, 115 patients with small mutations (18.49% of all patients) were included and DMD mutations were detected by either Sanger (53.91%) or next generation sequencing (46.09%). In total, 106 small mutations were detected, 28 of which (26.42%) had not been reported previously. The most common mutations were nonsense mutations (52.17%), followed by splicing (24.35%), frameshift (17.39%), and missense mutations (5.22%), in addition to a single untranslated region mutation (0.87%). We discovered distinct mutation characteristics in our patients, such as different positional distributions, indicating different exon skipping therapy strategies for small mutations in Chinese patients. Almost all patients (96.51%) with truncating or missense mutations, were covered by triple/double/single-exon skipping therapy; the most frequent single-exon skipping strategy was skipping exon 32, applicable for 8.51% of patients. Furthermore, splicing classification grades were correlated with phenotypes in nonsense mutations (P < 0.001), and serum creatinine levels differed significantly between DMD/IMD and BMD for patients ≤ 16 years old (P = 0.002). These observations can further aid prognostic judgment and guide treatment. In conclusion, the mutation characteristics and genotype–phenotype correlations in Chinese patients with dystrophinopathies and small mutations could provide insights into the molecular mechanisms of pathogenesis, diagnosis, and treatment designs